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Multimodal LLMs Predict Urban Safety but Show Demographic Bias

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the capabilities of Multimodal Large Language Models (MLLMs) in assessing perceived urban safety from street-view imagery. While these models demonstrate a zero-shot capability to predict safety with reasonable accuracy across various cities, they exhibit a tendency to favor 'Safe' classifications and underpredict unsafety. Furthermore, the study reveals that MLLMs encode non-neutral demographic priors, showing significant shifts in safety perception when prompted with specific gender, age, or racial/ethnic personas. AI

IMPACT Reveals that MLLMs can be used to assess urban safety but carry demographic biases, impacting their neutrality in planning applications.

RANK_REASON Research paper published on arXiv detailing findings about MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Multimodal LLMs Predict Urban Safety but Show Demographic Bias

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27 / 100
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Research paper published on arXiv detailing findings about MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ciro Beneduce, Bruno Lepri, Massimiliano Luca ·

    Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors

    arXiv:2503.00610v2 Announce Type: replace-cross Abstract: Understanding how people perceive urban environments is essential for inclusive planning, yet conventional surveys are costly and difficult to scale. We investigate whether Multimodal Large Language Models (MLLMs) can asse…

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